What Questions Should a Company Ask Before Adopting an AI Vendor?

Vendor Evaluation Starts With Data, Not Features

It’s easy for a company evaluating an AI vendor to focus entirely on what the tool can do — the demo, the feature list, the promised time savings. But the more consequential questions are usually about what happens to the company’s data once it enters the vendor’s system. Does the vendor store submitted data, and for how long? Is that data used to train or improve the vendor’s underlying models, and if so, is there an option to opt out? Who at the vendor, or which subprocessors, can access that data? These questions matter because once information is shared with an AI vendor, the company has limited ability to control what happens to it afterward unless the contract spells it out clearly.

Getting clear, specific answers — ideally in writing, in the contract or terms of service, rather than a verbal assurance from a salesperson — is worth insisting on before any sensitive company or customer data is involved.

Reliability, Compliance, and the Fine Print

Beyond data handling, a company should ask pointed questions about the tool’s reliability limitations. AI systems, particularly generative ones, can produce inaccurate or fabricated output, and a credible vendor should be able to describe known failure modes and what safeguards exist, rather than claiming the tool is always correct. It’s reasonable to ask how the vendor handles or discloses these limitations, and whether there’s a way to flag and report errors.

For companies in regulated industries — healthcare, finance, legal services — compliance questions become central. It’s worth asking directly whether the vendor supports relevant regulatory frameworks or has relevant certifications, and asking for documentation rather than a general assurance. A vendor unable or unwilling to answer specifics here is itself useful information.

Contract terms around data portability and deletion are also frequently overlooked. If the relationship ends, can the company retrieve its data in a usable format, and is the vendor obligated to delete company data from its systems afterward? Because AI vendors are a relatively new category compared to established enterprise software, these terms aren’t always as standardized as they are with traditional vendors, making it worth reading carefully rather than assuming familiar protections apply.

Putting It Into Practice

Consider a healthcare administrative company evaluating an AI tool to help draft patient communication. Beyond asking whether the tool writes well, the company would need to ask specifically whether patient information entered into the tool is used for model training, whether the vendor can support relevant healthcare privacy requirements, and what audit trail exists if an error in AI-drafted content led to a patient complaint. A vendor evaluation that skipped these questions in favor of just testing writing quality would be missing the considerations most likely to create real problems later.

Asking how frequently the vendor updates its underlying model, and whether the company will be notified of major changes, is another practical question — since a tool’s behavior on a given task can shift after a model update in a way that traditional software rarely does after a routine patch.

Bottom Line

Before adopting an AI vendor, a company should prioritize questions about data storage and training use, reliability limitations, compliance support, and contractual terms around data portability and deletion — since these areas, more than feature lists, determine the real risk and long-term cost of the relationship.

Estimate Your Time Savings

See how many hours and dollars using AI for a repeated task could save you with our free AI Time-Savings Calculator.

Go deeper

• What Happens to Student Data If an Ed-Tech AI Company Shuts Down?
• How do security teams evaluate a new ai tool before deploying it internally?
• Can Enterprise AI Platforms Guarantee Data Isolation?